
'''simple docstring'''
import os
from pathlib import Path
from unittest.mock import patch

import pytest
import zstandard as zstd

from datasets.download.download_config import DownloadConfig
from datasets.utils.file_utils import (
    OfflineModeIsEnabled,
    cached_path,
    fsspec_get,
    fsspec_head,
    ftp_get,
    ftp_head,
    get_from_cache,
    http_get,
    http_head,
)


lowerCAmelCase					:    str           = """\
    Text data.
    Second line of data."""

lowerCAmelCase					:    Optional[Any]           = """file"""







@pytest.fixture(scope='session')
def 		A_(     A						:		str):
							UpperCamelCase   			=     tmp_path_factory.mktemp('data') / (FILE_PATH + """.zstd""")
							UpperCamelCase   			=     bytes(a__		,     'utf-8')
							with zstd.open(a__		,     'wb') as f:
														f.write(a__)
							return path







@pytest.fixture
def 		A_(     A						:		Tuple):



							with open(os.path.join(tmpfs.local_root_dir		,     a__)		,     'w') as f:
														f.write(a__)
							return FILE_PATH







@pytest.mark.parametrize('compression_format'		,     ['gzip', 'xz', 'zstd'])
def 		A_(     A						:		Any		,     A						:		Union[str, Any]		,     A						:		List[Any]		,     A						:		List[Any]		,     A						:		Tuple		,     A						:		Any):
							UpperCamelCase   			=     {"""gzip""": gz_file, """xz""": xz_file, """zstd""": zstd_path}
							UpperCamelCase   			=     input_paths[compression_format]
							UpperCamelCase   			=     tmp_path / """cache"""
							UpperCamelCase   			=     DownloadConfig(cache_dir=a__		,     extract_compressed_file=a__)
							UpperCamelCase   			=     cached_path(a__		,     download_config=a__)
							with open(a__) as f:
														UpperCamelCase   			=     f.read()
							with open(a__) as f:
														UpperCamelCase   			=     f.read()
							assert extracted_file_content == expected_file_content







@pytest.mark.parametrize('default_extracted'		,     [True, False])
@pytest.mark.parametrize('default_cache_dir'		,     [True, False])
def 		A_(     A						:		Tuple		,     A						:		Optional[int]		,     A						:		Any		,     A						:		List[Any]		,     A						:		Dict):
							UpperCamelCase   			=     """custom_cache"""
							UpperCamelCase   			=     """custom_extracted_dir"""
							UpperCamelCase   			=     tmp_path / """custom_extracted_path"""
							if default_extracted:
														UpperCamelCase   			=     ("""downloads""" if default_cache_dir else custom_cache_dir, """extracted""")
							else:
														monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_DIR'		,     a__)
														monkeypatch.setattr('datasets.config.EXTRACTED_DATASETS_PATH'		,     str(a__))
														UpperCamelCase   			=     custom_extracted_path.parts[-2:] if default_cache_dir else (custom_cache_dir, custom_extracted_dir)

							UpperCamelCase   			=     xz_file
							UpperCamelCase   			=     (
							    DownloadConfig(extract_compressed_file=a__)
							    if default_cache_dir
							    else DownloadConfig(cache_dir=tmp_path / custom_cache_dir		,     extract_compressed_file=a__)
							)
							UpperCamelCase   			=     cached_path(a__		,     download_config=a__)
							assert Path(a__).parent.parts[-2:] == expected







def 		A_(     A						:		List[str]):
							UpperCamelCase   			=     str(Path(a__).resolve())
							assert cached_path(a__) == text_file
							# relative path
							UpperCamelCase   			=     str(Path(a__).resolve().relative_to(Path(os.getcwd())))
							assert cached_path(a__) == text_file







def 		A_(     A						:		int):
							UpperCamelCase   			=     str(tmp_path.resolve() / '__missing_file__.txt')
							with pytest.raises(a__):
														cached_path(a__)
							# relative path
							UpperCamelCase   			=     """./__missing_file__.txt"""
							with pytest.raises(a__):
														cached_path(a__)







def 		A_(     A						:		Any):
							UpperCamelCase   			=     get_from_cache(f'''tmp://{tmpfs_file}''')
							with open(a__) as f:
														UpperCamelCase   			=     f.read()
							assert output_file_content == FILE_CONTENT







@patch('datasets.config.HF_DATASETS_OFFLINE'		,     a__)
def 		A_(     ):



							with pytest.raises(a__):
														cached_path('https://huggingface.co')







@patch('datasets.config.HF_DATASETS_OFFLINE'		,     a__)
def 		A_(     A						:		Union[str, Any]):
							UpperCamelCase   			=     tmp_path_factory.mktemp('data') / """file.html"""
							with pytest.raises(a__):
														http_get('https://huggingface.co'		,     temp_file=a__)
							with pytest.raises(a__):
														http_head('https://huggingface.co')







@patch('datasets.config.HF_DATASETS_OFFLINE'		,     a__)
def 		A_(     A						:		Optional[int]):
							UpperCamelCase   			=     tmp_path_factory.mktemp('data') / """file.html"""
							with pytest.raises(a__):
														ftp_get('ftp://huggingface.co'		,     temp_file=a__)
							with pytest.raises(a__):
														ftp_head('ftp://huggingface.co')







@patch('datasets.config.HF_DATASETS_OFFLINE'		,     a__)
def 		A_(     A						:		Dict):
							UpperCamelCase   			=     tmp_path_factory.mktemp('data') / """file.html"""
							with pytest.raises(a__):
														fsspec_get('s3://huggingface.co'		,     temp_file=a__)
							with pytest.raises(a__):
														fsspec_head('s3://huggingface.co')







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= [
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """encoder.embed_positions._float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_							,					a_						:						List[Any] 							= emb.weight.shape
			a_						:						Dict 							= nn.Linear(a__  ,	a__  ,	bias=a__)
			a_						:						str 							= emb.weight.data
			return lin_layer


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= torch.load(a__  ,	map_location="""cpu""")
			a_						:						int 							= mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
			a_						:						Dict 							= mam_aaa["""model"""]
			remove_ignore_keys_(a__)
			a_						:						List[str] 							= state_dict["""encoder.embed_tokens.weight"""].shape[0]

			a_						:						Union[str, Any] 							= MaMaaaConfig(
			    vocab_size=a__  ,	max_position_embeddings=1_0_2_4  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="""relu"""  ,	)

			a_						:						Union[str, Any] 							= state_dict["""decoder.embed_tokens.weight"""]
			a_						:						List[Any] 							= MaMaaaForConditionalGeneration(a__)
			model.model.load_state_dict(a__  ,	strict=a__)
			a_						:						Any 							= make_linear_from_emb(model.model.shared)

			return model


if __name__ == "__main__":
	__snake_case				:      Union[str, Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	__snake_case				:      int   		=			parser.parse_args()
	__snake_case				:      Any   		=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)


